scholarly journals Snow-to-Liquid Ratio Variability and Prediction at a High-Elevation Site in Utah’s Wasatch Mountains

2010 ◽  
Vol 25 (1) ◽  
pp. 323-337 ◽  
Author(s):  
Trevor I. Alcott ◽  
W. James Steenburgh
Keyword(s):  
North American ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Predictive Ability ◽  
High Elevation ◽  
Reanalysis Data ◽  
Precipitation Forecast ◽  
Atmospheric Conditions ◽  
Liquid Ratio ◽  
High Elevation Site

Abstract Contemporary snowfall forecasting is a three-step process involving a quantitative precipitation forecast (QPF), determination of precipitation type, and application of a snow-to-liquid ratio (SLR). The final step is often performed using climatology or algorithms based primarily on temperature. Based on a record of consistent and professional daily snowfall measurements, this study 1) presents general characteristics of SLR at Alta, Utah, a high-elevation site in interior North America with frequent winter storms; 2) diagnoses relationships between SLR and atmospheric conditions using reanalysis data; and 3) develops a statistical method for predicting SLR at the study location. The mean SLR at Alta is similar to that observed at lower elevations in the surrounding region, with substantial variability throughout the winter season. Using data from the North American Regional Reanalysis, temperature, wind speed, and midlevel relative humidity are shown to be related to SLR, with the strongest correlation occurring between SLR and near-crest-level (650 hPa) temperature. A stepwise multiple linear regression (SMLR) equation is constructed that explains 68% of the SLR variance for all events, and 88% for a high snow-water equivalent (>25 mm) subset. To test predictive ability, the straightforward SMLR approach is applied to archived 12–36-h forecasts from the National Centers for Environmental Prediction Eta/North American Mesoscale (Eta/NAM) model, yielding an improvement over existing operational SLR prediction techniques. Errors in QPF over complex terrain, however, ultimately limit skill in forecasting snowfall amount.

scholarly journals Characterization of Airborne Microbial Communities at a High-Elevation Site and Their Potential To Act as Atmospheric Ice Nuclei

2009 ◽  
Vol 75 (15) ◽  
pp. 5121-5130 ◽  
Author(s):  
Robert M. Bowers ◽  
Christian L. Lauber ◽  
Christine Wiedinmyer ◽  
Micah Hamady ◽  
Anna G. Hallar ◽  
...  
Keyword(s):  
Microbial Communities ◽  
High Elevation ◽  
Small Subunit ◽  
Microbial Community Analysis ◽  
Rrna Gene ◽  
Atmospheric Conditions ◽  
Small Subunit Rrna ◽  
Ice Nuclei ◽  
High Elevation Site ◽  
Bacteria And Fungi

ABSTRACT Bacteria and fungi are ubiquitous in the atmosphere. The diversity and abundance of airborne microbes may be strongly influenced by atmospheric conditions or even influence atmospheric conditions themselves by acting as ice nucleators. However, few comprehensive studies have described the diversity and dynamics of airborne bacteria and fungi based on culture-independent techniques. We document atmospheric microbial abundance, community composition, and ice nucleation at a high-elevation site in northwestern Colorado. We used a standard small-subunit rRNA gene Sanger sequencing approach for total microbial community analysis and a bacteria-specific 16S rRNA bar-coded pyrosequencing approach (4,864 sequences total). During the 2-week collection period, total microbial abundances were relatively constant, ranging from 9.6 × 105 to 6.6 × 106 cells m−3 of air, and the diversity and composition of the airborne microbial communities were also relatively static. Bacteria and fungi were nearly equivalent, and members of the proteobacterial groups Burkholderiales and Moraxellaceae (particularly the genus Psychrobacter) were dominant. These taxa were not always the most abundant in freshly fallen snow samples collected at this site. Although there was minimal variability in microbial abundances and composition within the atmosphere, the number of biological ice nuclei increased significantly during periods of high relative humidity. However, these changes in ice nuclei numbers were not associated with changes in the relative abundances of the most commonly studied ice-nucleating bacteria.

Implementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests

2013 ◽  
Vol 52 (6) ◽  
pp. 1458-1476 ◽  
Author(s):  
Lulin Xue ◽  
Sarah A. Tessendorf ◽  
Eric Nelson ◽  
Roy Rasmussen ◽  
Daniel Breed ◽  
...  
Keyword(s):  
Silver Iodide ◽  
Winter Season ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Atmospheric Conditions ◽  
Cloud Seeding ◽  
The North ◽  
Cloud Properties ◽  
Regional Reanalysis ◽  
Seeding Effects

AbstractFour cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.

scholarly journals Time-series analysis of passive-microwave-derived central North American snow water equivalent imagery

2002 ◽  
Vol 34 ◽  
pp. 1-7 ◽  
Author(s):  
C. Derksen ◽  
A. Walker ◽  
E. LeDrew ◽  
B. Goodison
Keyword(s):  
Time Series ◽  
Land Cover ◽  
North American ◽  
Snow Water Equivalent ◽  
Winter Season ◽  
Weighted Average ◽  
Passive Microwave ◽  
Central Canada ◽  
Snow Water ◽  
Total Study Area

AbstractThe Meteorological Service of Canada has developed a series of operational snow water equivalent (SWE) retrieval algorithms for central Canada, based on the vertically polarized difference index for the 19 and 37 GHz channels of the Special Sensor Microwave/Imager (SSM/I). Separate algorithms derive SWE for open environments, deciduous, coniferous and sparse forest cover. A final SWE value represents the area-weighted average based on the proportional land cover within each pixel. In this study, 5 day averaged (pentad) passive-microwave-derived SWE imagery for the winter season (December–February) of 1994/95 is compared to in situ data from central Canada in order to assess algorithm performance. Investigation of regions with varying proportional land cover within the four algorithm classes shows that retrieved SWE remains within ±10–20mm of surface observations, independent of fractional within-pixel land cover. Following algorithm evaluation, ten winter seasons (1988/89 through 1997/98) of pentad central North American SWE imagery are subjected to a rotated principal-component analysis (PCA). Although there are no trends in total study-area SWE, the PCA results identify the interseasonal variability in the SWE accumulation and ablation centers of action through the SSM/I time series.

Stopover Site Fidelity by Tennessee Warblers at a Southern Appalachian High-elevation Site

2012 ◽  
Vol 124 (2) ◽  
pp. 366-370 ◽  
Author(s):  
David F. Vogt ◽  
Mark E. Hopey ◽  
G. Rad Mayfield ◽  
Eric C. Soehren ◽  
Laura M. Lewis ◽  
...  
Keyword(s):  
Site Fidelity ◽  
High Elevation ◽  
Stopover Site ◽  
Southern Appalachian ◽  
High Elevation Site

scholarly journals Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 890
Author(s):  
Mohamed Wassim Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
River Discharge ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Water Runoff ◽  
High Atlas ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

scholarly journals Comparing Sudden Stratospheric Warming Definitions in Reanalysis Data*

2015 ◽  
Vol 28 (17) ◽  
pp. 6823-6840 ◽  
Author(s):  
Froila M. Palmeiro ◽  
David Barriopedro ◽  
Ricardo García-Herrera ◽  
Natalia Calvo
Keyword(s):  
Seasonal Cycle ◽  
Winter Season ◽  
Reanalysis Data ◽  
Sudden Stratospheric Warming ◽  
Pronounced Increase ◽  
Future Studies ◽  
Common Peak ◽  
Before And After ◽  
The Mean ◽  
Large Case

Abstract Sudden stratospheric warmings (SSWs) are characterized by a pronounced increase of the stratospheric polar temperature during the winter season. Different definitions have been used in the literature to diagnose the occurrence of SSWs, yielding discrepancies in the detected events. The aim of this paper is to compare the SSW climatologies obtained by different methods using reanalysis data. The occurrences of Northern Hemisphere SSWs during the extended-winter season and the 1958–2014 period have been identified for a suite of eight representative definitions and three different reanalyses. Overall, and despite the differences in the number and exact dates of occurrence of SSWs, the main climatological signatures of SSWs are not sensitive to the considered reanalysis. The mean frequency of SSWs is 6.7 events decade−1, but it ranges from 4 to 10 events, depending on the method. The seasonal cycle of events is statistically indistinguishable across definitions, with a common peak in January. However, the multidecadal variability is method dependent, with only two definitions displaying minimum frequencies in the 1990s. An analysis of the mean signatures of SSWs in the stratosphere revealed negligible differences among methods compared to the large case-to-case variability within a given definition. The stronger and more coherent tropospheric signals before and after SSWs are associated with major events, which are detected by most methods. The tropospheric signals of minor SSWs are less robust, representing the largest source of discrepancy across definitions. Therefore, to obtain robust results, future studies on stratosphere–troposphere coupling should aim to minimize the detection of minor warmings.

scholarly journals Nimbus-7 SMMR Derived Global Snow Cover Parameters

1987 ◽  
Vol 9 ◽  
pp. 39-44 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Theoretical Calculations ◽  
Winter Season ◽  
Western China ◽  
High Plains ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

scholarly journals A new moisture tagging capability in the Weather Research and Forecasting Model: formulation, validation and application to the 2014 Great Lake-effect snowstorm

2017 ◽  
Author(s):  
Damián Insua-Costa ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Great Lake ◽  
Snow Water Equivalent ◽  
Precipitable Water ◽  
Weather Research And Forecasting ◽  
Atmospheric Conditions ◽  
Meteorological Model ◽  
Moisture Sources ◽  
Lake Effect ◽  
Snow Water ◽  
Weather Research

Abstract. A new moisture-tagging tool, usually known as water vapor tracer (WVT) method or online Eulerian method, has been implemented into the Weather Research and Forecasting (WRF) regional meteorological model, enabling it for precise studies on atmospheric moisture sources and pathways. We present here the method and its formulation, along with details of the implementation into WRF. We perform an in-depth validation with monthly long simulations over North America at 20 km resolution, tagging all possible moisture sources: lateral boundaries, continental, maritime or lake surfaces and initial atmospheric conditions. We estimate errors as the moisture or precipitation amounts that cannot be traced back to any source. Validation results indicate that the method exhibits high precision, with errors considerably lower than 1 % during the entire simulation period, for both precipitation and total precipitable water. We apply the method to the Great Lake-effect snowstorm of November 2014, aiming at quantifying the contribution of lake evaporation to the large snow accumulations observed in the event. We perform simulations in a nested domain at 5 km resolution with the tagging technique, demonstrating that about 30–50 % of precipitation in the regions immediately downwind, originated from evaporated moisture in the Great Lakes. This contribution increases to between 50–60 % of the snow water equivalent in the most severely affected areas, which suggests that evaporative fluxes from the lakes have a fundamental role in producing the most extreme accumulations in these episodes, resulting in the highest socio-economic impacts.

scholarly journals Empirical atmospheric thresholds for debris flows and flash floods in the southern French Alps

2014 ◽  
Vol 14 (6) ◽  
pp. 1517-1530 ◽  
Author(s):  
T. Turkington ◽  
J. Ettema ◽  
C. J. van Westen ◽  
K. Breinl
Keyword(s):  
Debris Flows ◽  
Warning System ◽  
Flash Floods ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Atmospheric Conditions ◽  
Convective Rainfall ◽  
French Alps ◽  
Mountainous Regions

Abstract. Debris flows and flash floods are often preceded by intense, convective rainfall. The establishment of reliable rainfall thresholds is an important component for quantitative hazard and risk assessment, and for the development of an early warning system. Traditional empirical thresholds based on peak intensity, duration and antecedent rainfall can be difficult to verify due to the localized character of the rainfall and the absence of weather radar or sufficiently dense rain gauge networks in mountainous regions. However, convective rainfall can be strongly linked to regional atmospheric patterns and profiles. There is potential to employ this in empirical threshold analysis. This work develops a methodology to determine robust thresholds for flash floods and debris flows utilizing regional atmospheric conditions derived from ECMWF ERA-Interim reanalysis data, comparing the results with rain-gauge-derived thresholds. The method includes selecting the appropriate atmospheric indicators, categorizing the potential thresholds, determining and testing the thresholds. The method is tested in the Ubaye Valley in the southern French Alps (548 km2), which is known to have localized convection triggered debris flows and flash floods. This paper shows that instability of the atmosphere and specific humidity at 700 hPa are the most important atmospheric indicators for debris flows and flash floods in the study area. Furthermore, this paper demonstrates that atmospheric reanalysis data are an important asset, and could replace rainfall measurements in empirical exceedance thresholds for debris flows and flash floods.

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